Automata learning is a concept discussed in the literature for decades. Accordingly, the theoretical framework for learning automata from observations has been in place already for a considerable time. Despite the ever-increasing theoretical maturity of the field, real-life applications are few and far
between. In part this can certainly be attributed to the lack of ready-made
infrastructure, e.g., frameworks that support automata learning with the goal of
learning realistic systems. Additionally, the degree of automation in this field
is low, meaning that learning setups have to be instantiated manually and per-system, making this a time-consuming and laborious undertaking.
The central question of this thesis is "How can active automata learning be readied for application on real-life systems?". Contributions presented includes work on learning frameworks and tools, learning algorithms, scalability of learning solutions, and automated configuration and execution of learning setups.